R/kmeans.R

In pentalibra/ggcluster: Tools for creating cluster plots, tree plots and dendrograms using ggplot.

## Cluster analysis helper functions
## 
## Author: Andrie
################################################################################
#
#

#' Extracts cluster allocation from object of class `kmeans`.
#' 
#' Extracts the cluster allocation from a \code{\link{kmeans}} analysis into a data.frame
#' 
#' @param model object of type kmeans
#' @param ... ignored
#' @seealso \code{\link{cluster_data}}, \code{\link{plot_cluster}}
#' @method cluster_data kmeans
#' @return
#' A data frame with the following columns:
#' \item{cluster}{Cluster allocation}
#' @export
#' @examples 
#' data(iris)
#' iris <- iris[, -5]
#' model <- kmeans(iris, 3)
#' str(cluster_data(model))
cluster_data.kmeans <- function(model, ...){
	data.frame(cluster=model$cluster)
}





##' Creates ellipsoid from dataframe containing x and y .
##' 
##' Generic function to extract cluster labels from a model into a data frame.
##' 
##' @param data A data frame containing at least three columns: x, y and cluster.
##' x and y represent the plotting coordinates, and cluster is the cluster allocation.
##' @param x Character string, indicating the x coordinate
##' @param y Character string, indicating the y coordinate
##' @param cluster Character string, indicating the cluster allocation
##' @seealso \code{\link{cluster_data}}, \code{\link{plot_cluster}}
##' @export
##' @examples
##' data(iris)
##' d <- iris[, -5]
##' model <- kmeans(d, 3)
##' cdata <- cluster_data(model)
##' pc <- princomp(d)
##' pcdata <- cluster_data(pc)
##' eedata <- cbind(cdata, pcdata)
##' eldata <- ellipsoid_data(eedata)
##' 
##' ggplot() + 
##' 		geom_point(data=cbind(cdata, pcdata), 
##' 			aes(x=x, y=y, colour=factor(cluster))) +
##' 		geom_polygon(data=eldata, 
##' 			aes(x=x, y=y, colour=factor(cluster), group=cluster), 
##' 			alpha=0.1)
#ellipsoid_data <- function(data, x="x", y="y", cluster="cluster"){
#	get_ellipse <- function(clustn){
#		sdata <- data[data[, cluster]==clustn, c(x, y)]
#		edata <- as.matrix(sdata)
#		ehull <- ellipsoidhull(edata)
#		phull <- as.data.frame(predict(ehull))
#		data.frame(x=phull$V1, y=phull$y, cluster=clustn)
#	}
#	ldply(unique(data[, cluster]), get_ellipse)
#}
#
#